{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e112c7ba",
   "metadata": {},
   "source": [
    "# DAY 48"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ae02cd",
   "metadata": {},
   "source": [
    "在继续讲解模块消融前，先补充几个之前没提的基础概念\n",
    "\n",
    "尤其需要搞懂张量的维度、以及计算后的维度，这对于你未来理解复杂的网络至关重要"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b30ae4",
   "metadata": {},
   "source": [
    "## 一、 随机张量的生成"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e1c90e",
   "metadata": {},
   "source": [
    "在深度学习中经常需要随机生成一些张量，比如权重的初始化，或者计算输入纬度经过模块后输出的维度，都可以用一个随机函数来实现需要的张量格式，而无需像之前一样必须加载一张真实的图片。\n",
    "\n",
    "随机函数的种类很多，我们了解其中一种即可，毕竟目的主要就是生成，对分布要求不重要。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a59059",
   "metadata": {},
   "source": [
    "### 1.1 torch.randn函数\n",
    "在 PyTorch 中，torch.randn()是一个常用的随机张量生成函数，它可以创建一个由标准正态分布（均值为 0，标准差为 1）随机数填充的张量。这种随机张量在深度学习中非常实用，常用于初始化模型参数、生成测试数据或模拟输入特征。\n",
    "\n",
    "torch.randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) \n",
    "- size：必选参数，表示输出张量的形状（如(3, 4)表示 3 行 4 列的矩阵）。\n",
    "- dtype：可选参数，指定张量的数据类型（如torch.float32、torch.int64等）。\n",
    "- device：可选参数，指定张量存储的设备（如'cpu'或'cuda'）。\n",
    "- requires_grad：可选参数，是否需要计算梯度（常用于训练模型时）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "366f4d9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标量: -1.6167410612106323, 形状: torch.Size([])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "# 生成标量（0维张量）\n",
    "scalar = torch.randn(())\n",
    "print(f\"标量: {scalar}, 形状: {scalar.shape}\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "14a0bcfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "向量: tensor([-1.9524,  0.5900,  0.7467, -1.8307,  0.4263]), 形状: torch.Size([5])\n"
     ]
    }
   ],
   "source": [
    "# 生成向量（1维张量）\n",
    "vector = torch.randn(5)  # 长度为5的向量\n",
    "print(f\"向量: {vector}, 形状: {vector.shape}\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1c0398fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "矩阵：tensor([[ 0.0283,  0.7692,  0.2744, -1.6120],\n",
      "        [ 0.3726,  1.5382, -1.0128,  0.4129],\n",
      "        [ 0.4898,  1.4782,  0.2019,  0.0863]]),矩阵形状: torch.Size([3, 4])\n"
     ]
    }
   ],
   "source": [
    "# 生成矩阵（2维张量）\n",
    "matrix = torch.randn(3, 4)  # 3行4列的矩阵\n",
    "print(f\"矩阵：{matrix},矩阵形状: {matrix.shape}\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b4112e02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3维张量形状: torch.Size([3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "# 生成3维张量（常用于图像数据的通道、高度、宽度）\n",
    "tensor_3d = torch.randn(3, 224, 224)  # 3通道，高224，宽224\n",
    "print(f\"3维张量形状: {tensor_3d.shape}\")  # 输出: torch.Size([3, 224, 224])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c6a7d7c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4维张量形状: torch.Size([2, 3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "# 生成4维张量（常用于批量图像数据：[batch, channel, height, width]）\n",
    "tensor_4d = torch.randn(2, 3, 224, 224)  # 批量大小为2，3通道，高224，宽224\n",
    "print(f\"4维张量形状: {tensor_4d.shape}\")  # 输出: torch.Size([2, 3, 224, 224])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9f63166",
   "metadata": {},
   "source": [
    "### 1.2 其他随机函数\n",
    "除了这些随机函数还有很多，自行了解，主要是生成数据的分布不同。掌握一个即可，掌握多了参数也记不住。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84a71521",
   "metadata": {},
   "source": [
    "torch.rand()：生成在 [0, 1) 范围内均匀分布的随机数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2f085094",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均匀分布随机数: tensor([[0.2089, 0.7786],\n",
      "        [0.1043, 0.1573],\n",
      "        [0.9637, 0.0397]]), 形状: torch.Size([3, 2])\n"
     ]
    }
   ],
   "source": [
    "x = torch.rand(3, 2)  # 生成3x2的张量\n",
    "print(f\"均匀分布随机数: {x}, 形状: {x.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30834c40",
   "metadata": {},
   "source": [
    "torch.randint()：生成指定范围内的随机整数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7dbf52f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机整数: tensor([3, 5, 7]), 形状: torch.Size([3])\n"
     ]
    }
   ],
   "source": [
    "x = torch.randint(low=0, high=10, size=(3,))  # 生成3个0到9之间的整数\n",
    "print(f\"随机整数: {x}, 形状: {x.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5f62ac",
   "metadata": {},
   "source": [
    "torch.normal()：生成指定均值和标准差的正态分布随机数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f813f449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正态分布随机数: tensor([ 0.1419, -1.5212]), 形状: torch.Size([2])\n"
     ]
    }
   ],
   "source": [
    "mean = torch.tensor([0.0, 0.0])\n",
    "std = torch.tensor([1.0, 2.0])\n",
    "x = torch.normal(mean, std)  # 生成两个正态分布随机数\n",
    "print(f\"正态分布随机数: {x}, 形状: {x.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a7902dad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[11, 22, 33],\n",
       "        [14, 25, 36]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一维张量与二维张量相加\n",
    "a = torch.tensor([[1, 2, 3], [4, 5, 6]])  # 形状: (2, 3)\n",
    "b = torch.tensor([10, 20, 30])             # 形状: (3,)\n",
    "\n",
    "# 广播后：b被扩展为[[10, 20, 30], [10, 20, 30]]\n",
    "result = a + b  \n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090bd58f",
   "metadata": {},
   "source": [
    "### 1.3 输出维度测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1ba39788",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入尺寸: torch.Size([1, 3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "# 生成输入张量 (批量大小, 通道数, 高度, 宽度)\n",
    "input_tensor = torch.randn(1, 3, 32, 32)  # 例如CIFAR-10图像\n",
    "print(f\"输入尺寸: {input_tensor.shape}\")  # 输出: [1, 3, 32, 32]"
   ]
  },
  {
   "attachments": {
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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "4d7ea043",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "![image-3.png](attachment:image-3.png)\n",
    "\n",
    "二维的卷积和池化计算公式是一致的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5e5a9592",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "卷积后尺寸: torch.Size([1, 16, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "# 1. 卷积层操作\n",
    "conv1 = nn.Conv2d(\n",
    "    in_channels=3,        # 输入通道数\n",
    "    out_channels=16,      # 输出通道数（卷积核数量）\n",
    "    kernel_size=3,        # 卷积核大小\n",
    "    stride=1,             # 步长\n",
    "    padding=1             # 填充\n",
    ")\n",
    "conv_output = conv1(input_tensor) # 由于 padding=1 且 stride=1，空间尺寸保持不变\n",
    "print(f\"卷积后尺寸: {conv_output.shape}\")  # 输出: [1, 16, 32, 32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a4628d90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "池化后尺寸: torch.Size([1, 16, 16, 16])\n"
     ]
    }
   ],
   "source": [
    "# 2. 池化层操作 (减小空间尺寸)\n",
    "pool = nn.MaxPool2d(kernel_size=2, stride=2) # 创建一个最大池化层\n",
    "pool_output = pool(conv_output)\n",
    "print(f\"池化后尺寸: {pool_output.shape}\")  # 输出: [1, 16, 16, 16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f4e426c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "展平后尺寸: torch.Size([1, 4096])\n"
     ]
    }
   ],
   "source": [
    "# 3. 将多维张量展平为向量\n",
    "flattened = pool_output.view(pool_output.size(0), -1)\n",
    "print(f\"展平后尺寸: {flattened.shape}\")  # 输出: [1, 4096] (16*16*16=4096)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "fead5bbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "线性层后尺寸: torch.Size([1, 128])\n"
     ]
    }
   ],
   "source": [
    "# 4. 线性层操作\n",
    "fc1 = nn.Linear(\n",
    "    in_features=4096,     # 输入特征数\n",
    "    out_features=128      # 输出特征数\n",
    ")\n",
    "fc_output = fc1(flattened)\n",
    "print(f\"线性层后尺寸: {fc_output.shape}\")  # 输出: [1, 128]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "517ea74c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最终输出尺寸: torch.Size([1, 10])\n",
      "tensor([[-0.3018, -0.4308,  0.3248,  0.2808,  0.5109, -0.0881, -0.0787, -0.0700,\n",
      "         -0.1004, -0.0580]], grad_fn=<AddmmBackward>)\n"
     ]
    }
   ],
   "source": [
    "# 5. 再经过一个线性层（例如分类器）\n",
    "fc2 = nn.Linear(128, 10)  # 假设是10分类问题\n",
    "final_output = fc2(fc_output)\n",
    "print(f\"最终输出尺寸: {final_output.shape}\")  # 输出: [1, 10]\n",
    "print(final_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1691f293",
   "metadata": {},
   "source": [
    "多分类问题通常使用Softmax，二分类问题用Sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a74c3c5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Softmax输出: tensor([[0.0712, 0.0626, 0.1332, 0.1275, 0.1605, 0.0882, 0.0890, 0.0898, 0.0871,\n",
      "         0.0909]], grad_fn=<SoftmaxBackward>)\n",
      "Softmax输出总和: 1.0000\n"
     ]
    }
   ],
   "source": [
    "# 使用Softmax替代Sigmoid\n",
    "softmax = nn.Softmax(dim=1)  # 在类别维度上进行Softmax\n",
    "class_probs = softmax(final_output)\n",
    "print(f\"Softmax输出: {class_probs}\")  # 总和为1的概率分布\n",
    "print(f\"Softmax输出总和: {class_probs.sum():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2aa2314",
   "metadata": {},
   "source": [
    "通过这种方法，可以很自然的看到每一层输出的shape，实际上在pycharm等非交互式环境ipynb时，可以借助断点+调试控制台不断测试维度信息，避免报错。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3137e26d",
   "metadata": {},
   "source": [
    "## 二、广播机制"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fd7aac",
   "metadata": {},
   "source": [
    "什么叫做广播机制\n",
    "\n",
    "PyTorch 的广播机制（Broadcasting）是一种强大的张量运算特性，允许在不同形状的张量之间进行算术运算，而无需显式地扩展张量维度或复制数据。这种机制使得代码更简洁高效，尤其在处理多维数据时非常实用。\n",
    "\n",
    "当对两个形状不同的张量进行运算时，PyTorch 会自动调整它们的形状，使它们在维度上兼容。具体规则如下：\n",
    "\n",
    "从右向左比较维度：PyTorch 从张量的最后一个维度开始向前比较，检查每个维度的大小是否相同或其中一个为 1。\n",
    "维度扩展规则：\n",
    "如果两个张量的某个维度大小相同，则继续比较下一个维度。\n",
    "如果其中一个张量的某个维度大小为 1，则该维度会被扩展为另一个张量对应维度的大小。\n",
    "如果两个张量的某个维度大小既不相同也不为 1，则会报错。\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "7e8d36e9",
   "metadata": {},
   "source": [
    "好的，我将为你润色和补全这段关于 PyTorch 广播机制的解释。以下是更清晰、完整的版本：\n",
    "\n",
    "\n",
    "### 2.1 PyTorch 广播机制\n",
    "\n",
    "PyTorch 的广播机制（Broadcasting）是一种高效的张量运算特性，允许在不同形状的张量之间执行元素级操作（如加法、乘法），而无需显式扩展或复制数据。这种机制通过自动调整张量维度来实现形状兼容，使代码更简洁、计算更高效。\n",
    "\n",
    "当对两个形状不同的张量进行运算时，PyTorch 会按以下规则自动处理维度兼容性：\n",
    "1. **从右向左比较维度**：PyTorch 从张量的最后一个维度（最右侧）开始向前逐维比较。\n",
    "2. **维度扩展条件**：\n",
    "   - **相等维度**：若两个张量在某一维度上大小相同，则继续比较下一维度。\n",
    "   - **一维扩展**：若其中一个张量在某一维度上大小为 **1**，则该维度会被扩展为另一个张量对应维度的大小。\n",
    "   - **不兼容错误**：若某一维度大小既不相同也不为 **1**，则抛出 `RuntimeError`。-----维度必须满足广播规则，否则会报错。\n",
    "3. **维度补全规则**：若一个张量的维度少于另一个，则在其**左侧补 1** 直至维度数匹配。\n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2343bb4",
   "metadata": {},
   "source": [
    "关注2个信息\n",
    "\n",
    "1. 广播后的尺寸变化\n",
    "2. 扩展后的值变化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc594398",
   "metadata": {},
   "source": [
    "### 2.1 加法的广播机制"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95a602c",
   "metadata": {},
   "source": [
    "二维张量与一维向量相加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ef99dd2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始张量a:\n",
      "tensor([[10],\n",
      "        [20],\n",
      "        [30]])\n",
      "\n",
      "原始张量b:\n",
      "tensor([1, 2, 3])\n",
      "\n",
      "广播后a的值扩展:\n",
      "tensor([[10, 10, 10],\n",
      "        [20, 20, 20],\n",
      "        [30, 30, 30]])\n",
      "\n",
      "广播后b的值扩展:\n",
      "tensor([[1, 2, 3],\n",
      "        [1, 2, 3],\n",
      "        [1, 2, 3]])\n",
      "\n",
      "加法结果:\n",
      "tensor([[11, 12, 13],\n",
      "        [21, 22, 23],\n",
      "        [31, 32, 33]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# 创建原始张量\n",
    "a = torch.tensor([[10], [20], [30]])  # 形状: (3, 1)\n",
    "b = torch.tensor([1, 2, 3])          # 形状: (3,)\n",
    "\n",
    "result = a + b\n",
    "# 广播过程\n",
    "# 1. b补全维度: (3,) → (1, 3)\n",
    "# 2. a扩展列: (3, 1) → (3, 3)\n",
    "# 3. b扩展行: (1, 3) → (3, 3)\n",
    "# 最终形状: (3, 3)\n",
    "\n",
    "\n",
    "print(\"原始张量a:\")\n",
    "print(a)\n",
    "\n",
    "\n",
    "print(\"\\n原始张量b:\")\n",
    "print(b)\n",
    "\n",
    "\n",
    "print(\"\\n广播后a的值扩展:\")\n",
    "print(torch.tensor([[10, 10, 10],\n",
    "                    [20, 20, 20],\n",
    "                    [30, 30, 30]]))  # 实际内存中未复制，仅逻辑上扩展\n",
    "\n",
    "print(\"\\n广播后b的值扩展:\")\n",
    "print(torch.tensor([[1, 2, 3],\n",
    "                    [1, 2, 3],\n",
    "                    [1, 2, 3]]))  # 实际内存中未复制，仅逻辑上扩展\n",
    "\n",
    "print(\"\\n加法结果:\")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1762ea",
   "metadata": {},
   "source": [
    "三维张量与二维张量相加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "37474845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始张量a:\n",
      "tensor([[[1],\n",
      "         [2]],\n",
      "\n",
      "        [[3],\n",
      "         [4]]])\n",
      "\n",
      "原始张量b:\n",
      "tensor([[10, 20]])\n",
      "\n",
      "广播后a的值扩展:\n",
      "tensor([[[1, 1],\n",
      "         [2, 2]],\n",
      "\n",
      "        [[3, 3],\n",
      "         [4, 4]]])\n",
      "\n",
      "广播后b的值扩展:\n",
      "tensor([[[10, 20],\n",
      "         [10, 20]],\n",
      "\n",
      "        [[10, 20],\n",
      "         [10, 20]]])\n",
      "\n",
      "加法结果:\n",
      "tensor([[[11, 21],\n",
      "         [12, 22]],\n",
      "\n",
      "        [[13, 23],\n",
      "         [14, 24]]])\n"
     ]
    }
   ],
   "source": [
    "# 创建原始张量\n",
    "a = torch.tensor([[[1], [2]], [[3], [4]]])  # 形状: (2, 2, 1)\n",
    "b = torch.tensor([[10, 20]])               # 形状: (1, 2)\n",
    "\n",
    "# 广播过程\n",
    "# 1. b补全维度: (1, 2) → (1, 1, 2)\n",
    "# 2. a扩展第三维: (2, 2, 1) → (2, 2, 2)\n",
    "# 3. b扩展第一维: (1, 1, 2) → (2, 1, 2)\n",
    "# 4. b扩展第二维: (2, 1, 2) → (2, 2, 2)\n",
    "# 最终形状: (2, 2, 2)\n",
    "\n",
    "result = a + b\n",
    "print(\"原始张量a:\")\n",
    "print(a)\n",
    "\n",
    "\n",
    "print(\"\\n原始张量b:\")\n",
    "print(b)\n",
    "\n",
    "\n",
    "print(\"\\n广播后a的值扩展:\")\n",
    "print(torch.tensor([[[1, 1],\n",
    "                     [2, 2]],\n",
    "                    [[3, 3],\n",
    "                     [4, 4]]]))  # 实际内存中未复制，仅逻辑上扩展\n",
    "\n",
    "print(\"\\n广播后b的值扩展:\")\n",
    "print(torch.tensor([[[10, 20],\n",
    "                     [10, 20]],\n",
    "                    [[10, 20],\n",
    "                     [10, 20]]]))  # 实际内存中未复制，仅逻辑上扩展\n",
    "\n",
    "print(\"\\n加法结果:\")\n",
    "print(result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5472d7e2",
   "metadata": {},
   "source": [
    "二维张量与标量相加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "824cec25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始张量a:\n",
      "tensor([[1, 2],\n",
      "        [3, 4]])\n",
      "\n",
      "标量b:\n",
      "10\n",
      "\n",
      "广播后b的值扩展:\n",
      "tensor([[10, 10],\n",
      "        [10, 10]])\n",
      "\n",
      "加法结果:\n",
      "tensor([[11, 12],\n",
      "        [13, 14]])\n"
     ]
    }
   ],
   "source": [
    "# 创建原始张量\n",
    "a = torch.tensor([[1, 2], [3, 4]])  # 形状: (2, 2)\n",
    "b = 10                              # 标量，形状视为 ()\n",
    "\n",
    "# 广播过程\n",
    "# 1. b补全维度: () → (1, 1)\n",
    "# 2. b扩展第一维: (1, 1) → (2, 1)\n",
    "# 3. b扩展第二维: (2, 1) → (2, 2)\n",
    "# 最终形状: (2, 2)\n",
    "\n",
    "result = a + b\n",
    "print(\"原始张量a:\")\n",
    "print(a)\n",
    "# 输出:\n",
    "# tensor([[1, 2],\n",
    "#         [3, 4]])\n",
    "\n",
    "print(\"\\n标量b:\")\n",
    "print(b)\n",
    "# 输出: 10\n",
    "\n",
    "print(\"\\n广播后b的值扩展:\")\n",
    "print(torch.tensor([[10, 10],\n",
    "                    [10, 10]]))  # 实际内存中未复制，仅逻辑上扩展\n",
    "\n",
    "print(\"\\n加法结果:\")\n",
    "print(result)\n",
    "# 输出:\n",
    "# tensor([[11, 12],\n",
    "#         [13, 14]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8562e3",
   "metadata": {},
   "source": [
    "高维张量与低维张量相加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3c4cecaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始张量a:\n",
      "tensor([[[1, 2],\n",
      "         [3, 4]]])\n",
      "\n",
      "原始张量b:\n",
      "tensor([[5, 6]])\n",
      "\n",
      "广播后b的值扩展:\n",
      "tensor([[[5, 6],\n",
      "         [5, 6]]])\n",
      "\n",
      "加法结果:\n",
      "tensor([[[ 6,  8],\n",
      "         [ 8, 10]]])\n"
     ]
    }
   ],
   "source": [
    "# 创建原始张量\n",
    "a = torch.tensor([[[1, 2], [3, 4]]])  # 形状: (1, 2, 2)\n",
    "b = torch.tensor([[5, 6]])            # 形状: (1, 2)\n",
    "\n",
    "# 广播过程\n",
    "# 1. b补全维度: (1, 2) → (1, 1, 2)\n",
    "# 2. b扩展第二维: (1, 1, 2) → (1, 2, 2)\n",
    "# 最终形状: (1, 2, 2)\n",
    "\n",
    "result = a + b\n",
    "print(\"原始张量a:\")\n",
    "print(a)\n",
    "# 输出:\n",
    "# tensor([[[1, 2],\n",
    "#          [3, 4]]])\n",
    "\n",
    "print(\"\\n原始张量b:\")\n",
    "print(b)\n",
    "# 输出:\n",
    "# tensor([[5, 6]])\n",
    "\n",
    "print(\"\\n广播后b的值扩展:\")\n",
    "print(torch.tensor([[[5, 6],\n",
    "                     [5, 6]]]))  # 实际内存中未复制，仅逻辑上扩展\n",
    "\n",
    "print(\"\\n加法结果:\")\n",
    "print(result)\n",
    "# 输出:\n",
    "# tensor([[[6, 8],\n",
    "#          [8, 10]]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cce713c",
   "metadata": {},
   "source": [
    "关键总结\n",
    "1. 尺寸变化：广播后的形状由各维度的最大值决定（示例 2 中最终形状为 (2, 2, 2)）。\n",
    "2. 值扩展：维度为 1 的张量通过复制扩展值（示例 1 中 b 从 [1, 2, 3] 扩展为三行相同的值）。\n",
    "3. 内存效率：扩展是逻辑上的，实际未复制数据，避免了内存浪费。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9112ebac",
   "metadata": {},
   "source": [
    "### 2.2 乘法的广播机制"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "e8db5057",
   "metadata": {},
   "source": [
    "矩阵乘法（@）的特殊规则\n",
    "\n",
    "矩阵乘法除了遵循通用广播规则外，还需要满足矩阵乘法的维度约束：\n",
    "\n",
    "最后两个维度必须满足：A.shape[-1] == B.shape[-2]（即 A 的列数等于 B 的行数）\n",
    "\n",
    "其他维度（批量维度）：遵循通用广播规则\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6924cd",
   "metadata": {},
   "source": [
    "批量矩阵与单个矩阵相乘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4eba7749",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A形状: torch.Size([2, 3, 4])\n",
      "B形状: torch.Size([4, 5])\n",
      "结果形状: torch.Size([2, 3, 5])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# A: 批量大小为2，每个是3×4的矩阵\n",
    "A = torch.randn(2, 3, 4)  # 形状: (2, 3, 4)\n",
    "\n",
    "# B: 单个4×5的矩阵\n",
    "B = torch.randn(4, 5)     # 形状: (4, 5)\n",
    "\n",
    "# 广播过程：\n",
    "# 1. B补全维度: (4, 5) → (1, 4, 5)\n",
    "# 2. B扩展第一维: (1, 4, 5) → (2, 4, 5)\n",
    "# 矩阵乘法: (2, 3, 4) @ (2, 4, 5) → (2, 3, 5)\n",
    "result = A @ B            # 结果形状: (2, 3, 5)\n",
    "\n",
    "print(\"A形状:\", A.shape)  # 输出: torch.Size([2, 3, 4])\n",
    "print(\"B形状:\", B.shape)  # 输出: torch.Size([4, 5])\n",
    "print(\"结果形状:\", result.shape)  # 输出: torch.Size([2, 3, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248b531f",
   "metadata": {},
   "source": [
    "批量矩阵与批量矩阵相乘（部分广播）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "bcf40414",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A形状: torch.Size([3, 2, 4])\n",
      "B形状: torch.Size([1, 4, 5])\n",
      "结果形状: torch.Size([3, 2, 5])\n"
     ]
    }
   ],
   "source": [
    "# A: 批量大小为3，每个是2×4的矩阵\n",
    "A = torch.randn(3, 2, 4)  # 形状: (3, 2, 4)\n",
    "\n",
    "# B: 批量大小为1，每个是4×5的矩阵\n",
    "B = torch.randn(1, 4, 5)  # 形状: (1, 4, 5)\n",
    "\n",
    "# 广播过程：\n",
    "# B扩展第一维: (1, 4, 5) → (3, 4, 5)\n",
    "# 矩阵乘法: (3, 2, 4) @ (3, 4, 5) → (3, 2, 5)\n",
    "result = A @ B            # 结果形状: (3, 2, 5)\n",
    "\n",
    "print(\"A形状:\", A.shape)  # 输出: torch.Size([3, 2, 4])\n",
    "print(\"B形状:\", B.shape)  # 输出: torch.Size([1, 4, 5])\n",
    "print(\"结果形状:\", result.shape)  # 输出: torch.Size([3, 2, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1be585",
   "metadata": {},
   "source": [
    "三维张量与二维张量相乘（高维广播）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "33fc118e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A形状: torch.Size([2, 3, 4, 5])\n",
      "B形状: torch.Size([5, 6])\n",
      "结果形状: torch.Size([2, 3, 4, 6])\n"
     ]
    }
   ],
   "source": [
    "# A: 批量大小为2，通道数为3，每个是4×5的矩阵\n",
    "A = torch.randn(2, 3, 4, 5)  # 形状: (2, 3, 4, 5)\n",
    "\n",
    "# B: 单个5×6的矩阵\n",
    "B = torch.randn(5, 6)        # 形状: (5, 6)\n",
    "\n",
    "# 广播过程：\n",
    "# 1. B补全维度: (5, 6) → (1, 1, 5, 6)\n",
    "# 2. B扩展第一维: (1, 1, 5, 6) → (2, 1, 5, 6)\n",
    "# 3. B扩展第二维: (2, 1, 5, 6) → (2, 3, 5, 6)\n",
    "# 矩阵乘法: (2, 3, 4, 5) @ (2, 3, 5, 6) → (2, 3, 4, 6)\n",
    "result = A @ B               # 结果形状: (2, 3, 4, 6)\n",
    "\n",
    "print(\"A形状:\", A.shape)     # 输出: torch.Size([2, 3, 4, 5])\n",
    "print(\"B形状:\", B.shape)     # 输出: torch.Size([5, 6])\n",
    "print(\"结果形状:\", result.shape)  # 输出: torch.Size([2, 3, 4, 6])"
   ]
  }
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